1. Field
Embodiments of the present disclosure relate to a localization method in which the position of a mobile apparatus is estimated using information detected through a plurality of sensors installed in the mobile apparatus.
2. Description of the Related Art
Localization is technique which allows a mobile apparatus itself to have space perception ability and becomes a key in implementation of an autonomous mobile function of implementation of augmented reality (AR) of the mobile apparatus. Here, the mobile apparatus includes a mobile robot (a robot cleaner or a walking robot) which has sensors corresponding to human eyes and having a detection function like a computer and thus autonomously travels without human manipulation, and a mobile device (a mobile communication device, such as a mobile phone) which does not have an autonomous mobile function but has a small size to allow a user to manipulate the device in hand and thus to carry and manipulate the device while moving.
Most localization technologies have been developed in the field of mobile robots having wheels. These technologies have a single filter structure using a Kalman filter or a particle filter as a key algorithm and are implemented through a simultaneous localization and mapping (SLAM) method. The position of a mobile robot is estimated by repeatedly performing a prediction step and an update step. In the prediction step, the position of the mobile robot in the next step is predicted using a robot motion model, and in the update step, position information of the mobile robot predicted using information of sensors is updated.
In the localization technologies, research regarding sensor fusion in which image sensors such as cameras or distance sensors such as laser sensors, ultrasonic sensors, etc., are installed in a robot main body and pieces of information acquired through the respective sensors are simultaneously processed, has been mainly developed. The robot simultaneously estimates position information of the robot and landmarks using feature points extracted from image information acquired through the cameras or corners, walls or a lattice map acquired through distance information detected through the laser sensors as natural landmarks.
In general, when localization through multiple sensor fusion is implemented, as the number of sensors increases, accuracy in localization is improved. In the conventional localization technologies having a single filter structure, as the number of sensors increases, the structure and implementation process of the filter become complicated, calculated information is concentrated on only one filter and thus information processing load of the filter increases. Particularly, in the update step, since the amount of measurement information increases in proportion to the number of mounted sensors, the amount of calculation of the filter increases and the operation speed of the filter decreases.
Further, in the case of the single filter structure, if the filter malfunctions or defective sensor information is input, there is no method to prevent such a defect and thus the filter is sensitive to disturbance. Consequently, a result of localization of the robot is easily diverged. In general, when the estimated value of the filter is diverged even once, it may be difficult to recover the original function of the filter, and if localization of the robot using image information is performed, the original function of the filter may be recovered using a separate technology named kidnap recovery but a calculation and implementation process may be complicated.